Building Data Teams 101: What's the Best Approach for Your Business?

Are you looking to start a data team but unsure where to begin? You're not alone. Every business is different, so the best way to organize your data team will vary. In this post, we'll break down the basics of building a data team and give you a few tips on what's worked well for other businesses.

Back to the basics: Know where you stand first

Before jumping into this field, you first need to analyze your company's needs for data, the level of data literacy within the organization, and the processes in place for collecting and accessing data quickly.

About hiring

You can hire your first data analyst whenever you want, but if you don't have concrete data collection processes, they'll spend most of their time trying to access and clean information. And that's a massive waste of somebody's talents.

The optimal first step is building a centralized data team and focusing on managing data for the entire company. This team will be responsible for setting up data infrastructure, developing data collection and maintenance processes, and establishing data analysis standards. You want someone who can extract data from different sources, clean it, and store it in a single place where your analyst can easily access it. You're thinking about data engineers.

A data engineer will help you build the foundation your data analyst will use to do their job. They will be responsible for managing data warehouses, developing data pipelines, and helping with data preparation to ensure that data is appropriately formatted and structured for analysis.

Hiring a data engineer will make your data team's efforts more manageable, and you will also benefit from having a diverse skill set in your team environment. A data engineer, for example, might develop a data pipeline that collects and cleans data from five different sources. With this help, the data analyst can spend more time on analysis than preparation.

A one-person data science team

Now, say that you're starting a data science team, it's crucial for you, but you don't have the budget for more than one person. In this case, you want to focus on hiring a data scientist who can wear many hats and is comfortable managing their own time.

Data scientists who can act as generalists, handling data collection, preparation, cleaning, and analysis independently, are valuable for any team. They should have a background in statistics to communicate findings to non-technical colleagues. Additionally, since they might need to fill the data engineer role sometimes, it is helpful if they also boast strong engineering skills.

Even though it might be tempting to hire just one data scientist that can do it all, this person would quickly become overwhelmed. Keep expectations in check, be clear about what you want the outcome to look like, and give them plenty of time and space to get their work done.

What to expect when managing data science teams

If you're the hiring manager, this next part is for you. It's crucial to comprehend that data scientists need time to investigate, experiment, and construct models when managing data science teams.

The best way to manage data science teams is by setting clear expectations, communicating often, and being open to feedback. Be prepared to give your team members the resources they need to do their job, including data access, computational power, and storage.

However, what's most important is that you give them the whole perspective of the company, the challenges the business is struggling with, and how data analytics could help solve those issues. Only then can they put their analysis into the context of the company and produce results that have a real impact.

How to structure a data science team

When it comes to structuring your team, there are two approaches people usually take: centralized or decentralized. Let's talk about them more in depth.

Centralized teams

A centralized data science team is one where all the members report to a single leader. This leader is responsible for managing the team, setting priorities, and ensuring everyone are working on the right things. They work as a center of excellence for the company, answering everyone's questions and following up with metrics to ensure the data science team's success.

This team structure is ideal if you're starting out in data science or your company is small. The centralized team can provide direction and assistance to the rest of the organization. In most cases, this team will be responsible for collecting and analyzing data and presenting their findings to other teams within the company.

The data science team will act as a 'Center of Excellence,' or in other words, they will answer questions from departments such as sales, marketing, and finance. When the teams receive answers to their questions, they often have follow-up inquiries about the initial response. However, as the company grows, so does the need for data science expertise, and the centralized team might become a bottleneck.

A centralized team structure might not be the best solution for a growing company. Instead, you might want to think about implementing a decentralized team model.

Decentralized teams

A decentralized data science team is one where each member works independently on their projects. This type of team is more common in larger organizations where multiple data science teams work on different things.

To put it differently, every data science team works separately on its projects with scarcely any communication with the other groups. This method can be successful if you have a definite plan of what each team should be occupied with and there's no requirement for teamwork. For example, if one group is in charge of constructing models and another group deploys them, there might not be many interactions between them.

The main advantage of this structure is that each team can move faster since they don't have to coordinate with other teams. The downside is that this can lead to duplicate work and a siloed approach to data science.

If you have a large organization, starting with a centralized team might make sense, and then moving to a decentralized model as the company grows. There's no right or wrong answer when choosing between a centralized or decentralized data science team; it all depends on your company's size, needs, and culture.

Hybrid approach

There's another way to structure a data science team: a hybrid one. Hybrid data science teams are a mix of both centralized and decentralized, where one leader manages the team, but each member has some autonomy over their projects.

This type of team can work well in companies that are somewhere between small and large. The centralized leader can provide guidance and ensure everyone is working on the right things, while the decentralized members can move quickly on their projects.

The hybrid approach can also be beneficial if you have a mix of senior and junior data scientists on the team. The old data scientists can act as the centralized leaders, providing mentorship and direction to the junior members, while the junior data scientists can work independently on their projects.

An alternative way to examine this hybrid method is to have a centralized data governance team working on infrastructure, access, and security tasks. Then appoint business analysts to other teams like sales, marketing, or finance so they can answer questions and improve processes with the support of the centralized team.